Heterologous production of (-)-geosmin in Saccharomyces cerevisiae.

(-)-Geosmin has high demand in perfumes and cosmetic products for its earthy congenial aroma. The current production of (-)-geosmin is either by distillation of sun-baked soil or by inefficient chemical synthesis because of the presence of multiple chiral centers. Fermentation processes are not viable as the titers of the Streptomyces sp. based processes are low. This work presents an alternative route by the heterologous synthesis of (-)-geosmin in Saccharomyces cerevisiae. The enzyme involved is the bifunctional geosmin synthase that catalyzes the conversion of farnesyl diphosphate to germacradienol and germacradienol to geosmin. This study evaluated the activity of many orthologs of geosmin synthase when expressed heterologously in S. cerevisiae. When the well-characterized CAB41566 from Streptomyces coelicolor origin was tested, germacradienol and germacrene D were detected but no geosmin. Bioinformatic analysis based on high/low identities to N-terminal and C-terminal domains of CAB41566 was carried out to identify different orthologs of geosmin synthase proteins from different bacterial and fungal origins. ADO68918 of Stigmatella aurantiaca origin showed the best activity among the tested orthologs, not only in terms of geosmin production but also an order of magnitude higher total abundance of the products of geosmin synthase as compared to CAB41566. This study successfully demonstrated the production of (-)-geosmin in S. cerevisiae and offers an alternative, sustainable and environment-friendly approach to producing (-)-geosmin.

Thermodynamics of Azo Dye Adsorption on a Newly Synthesized Titania-Doped Silica Aerogel by Cogelation: A Comparative Investigation with Silica Aerogels and Activated Charcoal.

The adsorption isotherms of azo dyes on a newly synthesized titania-doped silica (TdS) aerogel compared to silica aerogels and activated charcoal (AC) are systematically investigated. Monolithic TdS aerogels were synthesized by the cogelation process followed by supercritical drying of tetraethyl orthosilicate (TEOS) as a gel precursor and titanium(IV) isopropoxide (TTIP) as a metal complex precursor for co-polymerization in ethanol solvent. An acid-base catalyst was used for the hydrolysis and condensation of TEOS and TTIP. The effect of Ti4+ doping in a silica aerogel on the mesoporous structure and the adsorption capacity of methylene blue (MB) and crystal violet (CV) dyes were evaluated from the UV-vis absorption spectra. In order to compare the adsorption isotherms, the surface areas of silica and TdS aerogels were first normalized with respect to AC, as adsorption is a surface phenomenon. The azo dye equilibrium adsorption data were analyzed using different isotherm equations and found to follow the Langmuir adsorption isotherm. The maximum monolayer adsorption capacities for the adsorbent TdS aerogel normalized with the AC of the Langmuir isotherm are 131.58 and 159.89 mg/g for MB and CV dyes, respectively. From the Langmuir curve fitting, the Q max value of the TdS aerogel was found to increase by 1.22-fold compared to AC, while it increased 1.25-1.53-fold compared to the silica aerogel. After four cycles, regeneration efficiency values for MB and CV dyes are about 84 and 80%, respectively. The study demonstrates the excellent potential and recovery rate of silica and TdS aerogel adsorbents in removing dyes from wastewater. The pore volume and average pore size of the new aerogel, TdS, were found to be lower than those of the silica aerogel. Thus, a new TdS aerogel with a high capacity of adsorption of azo dyes is successfully achieved.

Thalamic control of sensory processing and spindles in a biophysical somatosensory thalamoreticular circuit model of wakefulness and sleep.

Thalamoreticular circuitry plays a key role in arousal, attention, cognition, and sleep spindles, and is linked to several brain disorders. A detailed computational model of mouse somatosensory thalamus and thalamic reticular nucleus has been developed to capture the properties of over 14,000 neurons connected by 6 million synapses. The model recreates the biological connectivity of these neurons, and simulations of the model reproduce multiple experimental findings in different brain states. The model shows that inhibitory rebound produces frequency-selective enhancement of thalamic responses during wakefulness. We find that thalamic interactions are responsible for the characteristic waxing and waning of spindle oscillations. In addition, we find that changes in thalamic excitability control spindle frequency and their incidence. The model is made openly available to provide a new tool for studying the function and dysfunction of the thalamoreticular circuitry in various brain states.

Geometry Controlled Oscillations in Liquid Crystal Polymer Films Triggered by Thermal Feedback.

Light-induced oscillatory behavior of liquid crystal polymer network (LCN) films has been demonstrated by several researchers in the past decade. Similarly, oscillations in LCN films under constant thermal stimulus have been reported recently, although the mechanism and the factors that govern the oscillatory behavior are not well understood. In this work, we study the dynamics of self-sustained oscillations exhibited by LCN films under a constant thermal stimulus through experiments and simulations. Geometrically asymmetric films such as a right triangle and an equilateral triangle are obtained from a twisted nematic square film. A multiphysics computational framework using the finite element method is developed to simulate the oscillatory behavior of the LCN films kept on a hot plate. The framework accounts for a coupling between heat transfer and mechanical deformations during the oscillations. Small temperature fluctuations (≈ 1 °C) coupled with gravity induced torque are shown to drive the oscillatory behavior at a specific plate temperature. We show for the first time that self-sustained oscillations can also be achieved in symmetric shapes, such as square films, by creating a thickness tapering between two opposite edges. The frequency of the oscillations is found to be in the range of 0.5 to 2.5 Hz for different geometries studied. The oscillation temperature depends on the mean thickness, size, and thickness profile of the films. As a possible application, we demonstrate a thermally actuated optical chopper using the oscillatory response of the films.

STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale.

Recent advances in computational neuroscience have demonstrated the usefulness and importance of stochastic, spatial reaction-diffusion simulations. However, ever increasing model complexity renders traditional serial solvers, as well as naive parallel implementations, inadequate. This paper introduces a new generation of the STochastic Engine for Pathway Simulation (STEPS) project (http://steps.sourceforge.net/), denominated STEPS 4.0, and its core components which have been designed for improved scalability, performance, and memory efficiency. STEPS 4.0 aims to enable novel scientific studies of macroscopic systems such as whole cells while capturing their nanoscale details. This class of models is out of reach for serial solvers due to the vast quantity of computation in such detailed models, and also out of reach for naive parallel solvers due to the large memory footprint. Based on a distributed mesh solution, we introduce a new parallel stochastic reaction-diffusion solver and a deterministic membrane potential solver in STEPS 4.0. The distributed mesh, together with improved data layout and algorithm designs, significantly reduces the memory footprint of parallel simulations in STEPS 4.0. This enables massively parallel simulations on modern HPC clusters and overcomes the limitations of the previous parallel STEPS implementation. Current and future improvements to the solver are not sustainable without following proper software engineering principles. For this reason, we also give an overview of how the STEPS codebase and the development environment have been updated to follow modern software development practices. We benchmark performance improvement and memory footprint on three published models with different complexities, from a simple spatial stochastic reaction-diffusion model, to a more complex one that is coupled to a deterministic membrane potential solver to simulate the calcium burst activity of a Purkinje neuron. Simulation results of these models suggest that the new solution dramatically reduces the per-core memory consumption by more than a factor of 30, while maintaining similar or better performance and scalability.

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance.

The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.

A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex.

Pyramidal cells (PCs) form the backbone of the layered structure of the neocortex, and plasticity of their synapses is thought to underlie learning in the brain. However, such long-term synaptic changes have been experimentally characterized between only a few types of PCs, posing a significant barrier for studying neocortical learning mechanisms. Here we introduce a model of synaptic plasticity based on data-constrained postsynaptic calcium dynamics, and show in a neocortical microcircuit model that a single parameter set is sufficient to unify the available experimental findings on long-term potentiation (LTP) and long-term depression (LTD) of PC connections. In particular, we find that the diverse plasticity outcomes across the different PC types can be explained by cell-type-specific synaptic physiology, cell morphology and innervation patterns, without requiring type-specific plasticity. Generalizing the model to in vivo extracellular calcium concentrations, we predict qualitatively different plasticity dynamics from those observed in vitro. This work provides a first comprehensive null model for LTP/LTD between neocortical PC types in vivo, and an open framework for further developing models of cortical synaptic plasticity.

Oxidative catalytic valorization of industrial lignin into phenolics: Effect of reaction parameters and metal oxides.

Oxidative depolymerization of an industrial lignin was performed to study the effect of various metal oxides in oxygen and air atmosphere. CeO2 exhibited excellent catalytic property, and promoted the production of bio-oil yield up to a maximum of 49 wt% in 10 bar O2, whereas 31 wt% bio-oil was noticed in atmospheric air. GC-MS analysis of bio-oil showed that high selectivity towards acetosyringone was observed in the presence of air (70.5 area%) as compared to oxygen (48.1 area%). Herein, we have also applied transitional metals (Co, Mn and Cu) doped CeO2 catalysts. Compared to Cu and Mn, Co metal showed efficient activity that promoted the breaking of labile ß-O-4 linkages via the conversion of Cα-OH in to carbonyl group in atmospheric air resulting in the formation of acetosyringone up to 78 area%. Moreover, it exhibited excellent catalytic activity up to four successive cycles. Catalyst has been characterized by XRD, BET, TEM, FT-IR and Raman spectroscopy.

Enrichment of Methylocystis dominant mixed culture from rice field for PHB production.

Presence of methanotrophs in diverse environmental habitats helps to reduce emissions of greenhouse gas like methane. Isolation and culture of undiscovered wealth of methanotrophic organisms can help in exploitation of these organisms in value added products. The present study focuses on the enrichment of methanotroph dominated mixed microbial community by use of three stage strategy of revival, proliferation, and segregation. During the enrichment process amplicon sequencing of 16 s rRNA V3-V4 region showed relative abundance of mixed culture comprising single methanotrophic species of Methylocystis genus (88.92%) along with only three other species. Methylocystis dominant mixed culture (MMI-11) was observed to produce polyhydroxyalkanoates (PHA). During studies to identify favourable culture conditions, nitrate was found to be preferred nitrogen source for growth and PHA production. Cell growth ability to produce PHA was also evaluated at 14 L fermentor by supplying gas using continuous bubbling and through pressurization in the headspace. The mixed methanotrophic culture was found to accumulate maximum of 22.20% polyhydroxybutyrate (PHB) under nitrate limited condition. The molecular weight of PHB was found to be 2.221 × 105 g mol-1 with polydispersity of 1.82.

Selection of methanotrophic platform for methanol production using methane and biogas.

To develop biotechnological process for methane to methanol conversion, selection of a suitable methanotrophic platform is an important aspect. Systematic approach based on literature and public databases was developed to select representative methanotrophs Methylotuvimicrobium alcaliphilum, Methylomonas methanica, Methylosinus trichosporium and Methylocella silvestris. Selected methanotrophs were further investigated for methanol tolerance and methanol production on pure methane as well as biogas along with key enzyme activities involved in methane utilization. Among selected methanotrophs M. alcaliphilum showed maximum methanol tolerance of 6% v/v along with maximum methanol production of 307.90 mg/L and 247.37 mg/L on pure methane and biogas respectively. Activity of methane monooxygenase and formate dehydrogenase enzymes in M.alcaliphilum was significantly higher up to 98.40 nmol/min/mg cells and 0.87 U/mg protein, respectively. Biotransformation trials in 14 L fermentor resulted in increased methanol production up to 418 and 331.20 mg/L, with yield coefficient 0.83 and 0.71 mg methanol/mg of pure methane and biogas respectively. The systematic selection resulted in haloalkaliphilic strain M. alcaliphilum as one of the potential methanotroph for bio-methanol production.

An efficient analytical reduction of detailed nonlinear neuron models.

Detailed conductance-based nonlinear neuron models consisting of thousands of synapses are key for understanding of the computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of these models are computationally expensive, considerably curtailing their utility. Neuron_Reduce is a new analytical approach to reduce the morphological complexity and computational time of nonlinear neuron models. Synapses and active membrane channels are mapped to the reduced model preserving their transfer impedance to the soma; synapses with identical transfer impedance are merged into one NEURON process still retaining their individual activation times. Neuron_Reduce accelerates the simulations by 40-250 folds for a variety of cell types and realistic number (10,000-100,000) of synapses while closely replicating voltage dynamics and specific dendritic computations. The reduced neuron-models will enable realistic simulations of neural networks at unprecedented scale, including networks emerging from micro-connectomics efforts and biologically-inspired "deep networks". Neuron_Reduce is publicly available and is straightforward to implement.

CoreNEURON : An Optimized Compute Engine for the NEURON Simulator.

The NEURON simulator has been developed over the past three decades and is widely used by neuroscientists to model the electrical activity of neuronal networks. Large network simulation projects using NEURON have supercomputer allocations that individually measure in the millions of core hours. Supercomputer centers are transitioning to next generation architectures and the work accomplished per core hour for these simulations could be improved by an order of magnitude if NEURON was able to better utilize those new hardware capabilities. In order to adapt NEURON to evolving computer architectures, the compute engine of the NEURON simulator has been extracted and has been optimized as a library called CoreNEURON. This paper presents the design, implementation, and optimizations of CoreNEURON. We describe how CoreNEURON can be used as a library with NEURON and then compare performance of different network models on multiple architectures including IBM BlueGene/Q, Intel Skylake, Intel MIC and NVIDIA GPU. We show how CoreNEURON can simulate existing NEURON network models with 4-7x less memory usage and 2-7x less execution time while maintaining binary result compatibility with NEURON.

Code Generation in Computational Neuroscience: A Review of Tools and Techniques.

Advances in experimental techniques and computational power allowing researchers to gather anatomical and electrophysiological data at unprecedented levels of detail have fostered the development of increasingly complex models in computational neuroscience. Large-scale, biophysically detailed cell models pose a particular set of computational challenges, and this has led to the development of a number of domain-specific simulators. At the other level of detail, the ever growing variety of point neuron models increases the implementation barrier even for those based on the relatively simple integrate-and-fire neuron model. Independently of the model complexity, all modeling methods crucially depend on an efficient and accurate transformation of mathematical model descriptions into efficiently executable code. Neuroscientists usually publish model descriptions in terms of the mathematical equations underlying them. However, actually simulating them requires they be translated into code. This can cause problems because errors may be introduced if this process is carried out by hand, and code written by neuroscientists may not be very computationally efficient. Furthermore, the translated code might be generated for different hardware platforms, operating system variants or even written in different languages and thus cannot easily be combined or even compared. Two main approaches to addressing this issues have been followed. The first is to limit users to a fixed set of optimized models, which limits flexibility. The second is to allow model definitions in a high level interpreted language, although this may limit performance. Recently, a third approach has become increasingly popular: using code generation to automatically translate high level descriptions into efficient low level code to combine the best of previous approaches. This approach also greatly enriches efforts to standardize simulator-independent model description languages. In the past few years, a number of code generation pipelines have been developed in the computational neuroscience community, which differ considerably in aim, scope and functionality. This article provides an overview of existing pipelines currently used within the community and contrasts their capabilities and the technologies and concepts behind them.

Pretreatment and enzymatic process modification strategies to improve efficiency of sugar production from sugarcane bagasse.

Pretreatment and enzymatic hydrolysis play a critical role in the economic production of sugars and fuels from lignocellulosic biomass. In this study, we evaluated diverse pilot-scale pretreatments and different post-pretreatment strategies for the production of fermentable sugars from sugarcane bagasse. For the pretreatment of bagasse at pilot-scale level, steam explosion without catalyst and combination of sulfuric and oxalic acids at low and high loadings were used. Subsequently, to enhance the efficiency of enzymatic hydrolysis of the pretreated bagasse, three different post-pretreatment process schemes were investigated. In the first scheme (Scheme 1), enzymatic hydrolysis was conducted on the whole pretreated slurry, without treatments such as washing or solid-liquid separation. In the second scheme (Scheme 2), the pretreated slurry was first pressure filtered to yield a solid and liquid phase. Following filtration, the separated liquid phase was remixed with the solid wet cake to generate slurry, which was then subsequently used for enzymatic hydrolysis. In the third scheme (Scheme 3), the pretreated slurry was washed with more water and filtered to obtain a solid and liquid phase, in which only the former was subjected to enzymatic hydrolysis. A 10 % higher enzymatic conversion was obtained in Scheme 2 than Scheme 1, while Scheme 3 resulted in only a 5-7 % increase due to additional washing unit operation and solid-liquid separation. Dynamic light scattering experiments conducted on post-pretreated bagasse indicate decrease of particle size due to solid-liquid separation involving pressure filtration provided increased the yield of C6 sugars. It is anticipated that different process modification methods used in this study before the enzymatic hydrolysis step can make the overall cellulosic ethanol process effective and possibly cost effective.

Reconstruction and Simulation of Neocortical Microcircuitry.

We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm(3) containing ~31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ~8 million connections with ~37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies. PAPERCLIP: VIDEO ABSTRACT.